6/15/2018

Input data

  • running phenograph on Cytotoxic T cells from non-“manual” samples
  • used following markers HLA.DR, IgD, CD19, CD3, CD4, CD8, CD45RA, CCR7, CD95, CD28, CD27

Phenograph Clusters detected

  • only within control sample “A”
  • n = 13 fcs files

Characterizing Phenograph Clusters

  • For each Phenograph Cluster:
    • compute median expression (centroid) of each input marker
  • group common centroids together

Characterizing Phenograph Clusters

  • “x”-axis bars are individual phenograph clusters
  • “y”-axis is the median expression of each marker within that cluster

Characterizing Phenograph Clusters

  • “x”-axis white lines separating individual CtlA .fcs files
  • “y”-axis is the median expression of each marker within that cluster

Characterizing Phenograph Clusters

  • expression “squished” to min of 0 and max of 250
  • outlier values are assigned either min or max

Characterizing Phenograph Clusters

  • “x”-axis ordered by common phenograph clusters
  • sorta grouped by “meta” clusters

Phenograph Clusters detected

  • all Ctl files
  • n = 55 fcs files

Characterizing Phenograph Clusters

  • “x”-axis white lines separating individual Ctls (multiple .fcs)

Characterizing Phenograph Clusters

  • “x”-axis ordered by common phenograph clusters

Phenograph Clusters detected

  • n = 1000 fcs files, 16200 clusters
  • non-“manual” and non-Ctls

Characterizing Phenograph Clusters

  • “x”-axis ordered by common phenograph clusters

Characterizing Phenograph Clusters

  • “x”-axis ordered by common phenograph clusters

Next Steps

  1. Use something similar to marker enrichment modeling (MEM) to describe clusters
  2. Output boolean matrices for visualization of populations in jFlow
  3. Limit/adjust/iterate which markers are used as phenograph input